{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# movielens电影评分数据分析(上)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从用户表读取用户信息\n",
    "users = pd.read_table('users.dat', header=None, names=['UserID','Gender','Age','Occupation','Zip-code'], sep='::',engine='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6040\n"
     ]
    }
   ],
   "source": [
    "# 打印列表长度，共有6040条记录\n",
    "print(len(users))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Zip-code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
       "      <td>16</td>\n",
       "      <td>70072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>55117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>7</td>\n",
       "      <td>02460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>55455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UserID Gender  Age  Occupation Zip-code\n",
       "0       1      F    1          10    48067\n",
       "1       2      M   56          16    70072\n",
       "2       3      M   25          15    55117\n",
       "3       4      M   45           7    02460\n",
       "4       5      M   25          20    55455"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看前五条记录\n",
    "users.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000209\n",
      "   UserID  MovieID  Rating  Timestamp\n",
      "0       1     1193       5  978300760\n",
      "1       1      661       3  978302109\n",
      "2       1      914       3  978301968\n",
      "3       1     3408       4  978300275\n",
      "4       1     2355       5  978824291\n",
      "3883\n",
      "   MovieID                               Title                        Genres\n",
      "0        1                    Toy Story (1995)   Animation|Children's|Comedy\n",
      "1        2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
      "2        3             Grumpier Old Men (1995)                Comedy|Romance\n",
      "3        4            Waiting to Exhale (1995)                  Comedy|Drama\n",
      "4        5  Father of the Bride Part II (1995)                        Comedy\n"
     ]
    }
   ],
   "source": [
    "# 同样方法，导入电影评分表\n",
    "ratings = pd.read_table('ratings.dat', header=None, names=['UserID', 'MovieID', 'Rating', 'Timestamp'], sep='::',engine='python')\n",
    "# 打印列表长度\n",
    "print(len(ratings))\n",
    "print(ratings.head(5))\n",
    "# 同样方法，导入电影数据表\n",
    "movies = pd.read_table('movies.dat', header=None, names=['MovieID', 'Title', 'Genres'], sep='::',engine='python')\n",
    "print(len(movies))\n",
    "print(movies.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 合并数据表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Zip-code</th>\n",
       "      <th>MovieID</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000204</th>\n",
       "      <td>5949</td>\n",
       "      <td>M</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>47901</td>\n",
       "      <td>2198</td>\n",
       "      <td>5</td>\n",
       "      <td>958846401</td>\n",
       "      <td>Modulations (1998)</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000205</th>\n",
       "      <td>5675</td>\n",
       "      <td>M</td>\n",
       "      <td>35</td>\n",
       "      <td>14</td>\n",
       "      <td>30030</td>\n",
       "      <td>2703</td>\n",
       "      <td>3</td>\n",
       "      <td>976029116</td>\n",
       "      <td>Broken Vessels (1998)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000206</th>\n",
       "      <td>5780</td>\n",
       "      <td>M</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>92886</td>\n",
       "      <td>2845</td>\n",
       "      <td>1</td>\n",
       "      <td>958153068</td>\n",
       "      <td>White Boys (1999)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000207</th>\n",
       "      <td>5851</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>55410</td>\n",
       "      <td>3607</td>\n",
       "      <td>5</td>\n",
       "      <td>957756608</td>\n",
       "      <td>One Little Indian (1973)</td>\n",
       "      <td>Comedy|Drama|Western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000208</th>\n",
       "      <td>5938</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>35401</td>\n",
       "      <td>2909</td>\n",
       "      <td>4</td>\n",
       "      <td>957273353</td>\n",
       "      <td>Five Wives, Three Secretaries and Me (1998)</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         UserID Gender  Age  Occupation Zip-code  MovieID  Rating  Timestamp  \\\n",
       "1000204    5949      M   18          17    47901     2198       5  958846401   \n",
       "1000205    5675      M   35          14    30030     2703       3  976029116   \n",
       "1000206    5780      M   18          17    92886     2845       1  958153068   \n",
       "1000207    5851      F   18          20    55410     3607       5  957756608   \n",
       "1000208    5938      M   25           1    35401     2909       4  957273353   \n",
       "\n",
       "                                               Title                Genres  \n",
       "1000204                           Modulations (1998)           Documentary  \n",
       "1000205                        Broken Vessels (1998)                 Drama  \n",
       "1000206                            White Boys (1999)                 Drama  \n",
       "1000207                     One Little Indian (1973)  Comedy|Drama|Western  \n",
       "1000208  Five Wives, Three Secretaries and Me (1998)           Documentary  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入完成之后，我们可以发现这三张表类似于数据库中的表\n",
    "# 要进行数据分析，我们就要将多张表进行合并才有助于分析 先将users与ratings两张表合并再跟movied合并\n",
    "data = pd.merge(pd.merge(users, ratings), movies)\n",
    "data.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对数据初步描述分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>MovieID</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000209e+06</td>\n",
       "      <td>1.000209e+06</td>\n",
       "      <td>1.000209e+06</td>\n",
       "      <td>1.000209e+06</td>\n",
       "      <td>1.000209e+06</td>\n",
       "      <td>1.000209e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.024512e+03</td>\n",
       "      <td>2.973831e+01</td>\n",
       "      <td>8.036138e+00</td>\n",
       "      <td>1.865540e+03</td>\n",
       "      <td>3.581564e+00</td>\n",
       "      <td>9.722437e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.728413e+03</td>\n",
       "      <td>1.175198e+01</td>\n",
       "      <td>6.531336e+00</td>\n",
       "      <td>1.096041e+03</td>\n",
       "      <td>1.117102e+00</td>\n",
       "      <td>1.215256e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.567039e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.506000e+03</td>\n",
       "      <td>2.500000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.030000e+03</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>9.653026e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.070000e+03</td>\n",
       "      <td>2.500000e+01</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>1.835000e+03</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>9.730180e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.476000e+03</td>\n",
       "      <td>3.500000e+01</td>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>2.770000e+03</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>9.752209e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.040000e+03</td>\n",
       "      <td>5.600000e+01</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>3.952000e+03</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.046455e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             UserID           Age    Occupation       MovieID        Rating  \\\n",
       "count  1.000209e+06  1.000209e+06  1.000209e+06  1.000209e+06  1.000209e+06   \n",
       "mean   3.024512e+03  2.973831e+01  8.036138e+00  1.865540e+03  3.581564e+00   \n",
       "std    1.728413e+03  1.175198e+01  6.531336e+00  1.096041e+03  1.117102e+00   \n",
       "min    1.000000e+00  1.000000e+00  0.000000e+00  1.000000e+00  1.000000e+00   \n",
       "25%    1.506000e+03  2.500000e+01  2.000000e+00  1.030000e+03  3.000000e+00   \n",
       "50%    3.070000e+03  2.500000e+01  7.000000e+00  1.835000e+03  4.000000e+00   \n",
       "75%    4.476000e+03  3.500000e+01  1.400000e+01  2.770000e+03  4.000000e+00   \n",
       "max    6.040000e+03  5.600000e+01  2.000000e+01  3.952000e+03  5.000000e+00   \n",
       "\n",
       "          Timestamp  \n",
       "count  1.000209e+06  \n",
       "mean   9.722437e+08  \n",
       "std    1.215256e+07  \n",
       "min    9.567039e+08  \n",
       "25%    9.653026e+08  \n",
       "50%    9.730180e+08  \n",
       "75%    9.752209e+08  \n",
       "max    1.046455e+09  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1000209 entries, 0 to 1000208\n",
      "Data columns (total 10 columns):\n",
      "UserID        1000209 non-null int64\n",
      "Gender        1000209 non-null object\n",
      "Age           1000209 non-null int64\n",
      "Occupation    1000209 non-null int64\n",
      "Zip-code      1000209 non-null object\n",
      "MovieID       1000209 non-null int64\n",
      "Rating        1000209 non-null int64\n",
      "Timestamp     1000209 non-null int64\n",
      "Title         1000209 non-null object\n",
      "Genres        1000209 non-null object\n",
      "dtypes: int64(6), object(4)\n",
      "memory usage: 83.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Zip-code</th>\n",
       "      <th>MovieID</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978300760</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1725</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>661</td>\n",
       "      <td>3</td>\n",
       "      <td>978302109</td>\n",
       "      <td>James and the Giant Peach (1996)</td>\n",
       "      <td>Animation|Children's|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>914</td>\n",
       "      <td>3</td>\n",
       "      <td>978301968</td>\n",
       "      <td>My Fair Lady (1964)</td>\n",
       "      <td>Musical|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2886</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>3408</td>\n",
       "      <td>4</td>\n",
       "      <td>978300275</td>\n",
       "      <td>Erin Brockovich (2000)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4201</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>2355</td>\n",
       "      <td>5</td>\n",
       "      <td>978824291</td>\n",
       "      <td>Bug's Life, A (1998)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5904</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>1197</td>\n",
       "      <td>3</td>\n",
       "      <td>978302268</td>\n",
       "      <td>Princess Bride, The (1987)</td>\n",
       "      <td>Action|Adventure|Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8222</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>1287</td>\n",
       "      <td>5</td>\n",
       "      <td>978302039</td>\n",
       "      <td>Ben-Hur (1959)</td>\n",
       "      <td>Action|Adventure|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8926</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>2804</td>\n",
       "      <td>5</td>\n",
       "      <td>978300719</td>\n",
       "      <td>Christmas Story, A (1983)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10278</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>594</td>\n",
       "      <td>4</td>\n",
       "      <td>978302268</td>\n",
       "      <td>Snow White and the Seven Dwarfs (1937)</td>\n",
       "      <td>Animation|Children's|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11041</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>919</td>\n",
       "      <td>4</td>\n",
       "      <td>978301368</td>\n",
       "      <td>Wizard of Oz, The (1939)</td>\n",
       "      <td>Adventure|Children's|Drama|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12759</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>595</td>\n",
       "      <td>5</td>\n",
       "      <td>978824268</td>\n",
       "      <td>Beauty and the Beast (1991)</td>\n",
       "      <td>Animation|Children's|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13819</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>938</td>\n",
       "      <td>4</td>\n",
       "      <td>978301752</td>\n",
       "      <td>Gigi (1958)</td>\n",
       "      <td>Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14006</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>2398</td>\n",
       "      <td>4</td>\n",
       "      <td>978302281</td>\n",
       "      <td>Miracle on 34th Street (1947)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14386</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>2918</td>\n",
       "      <td>4</td>\n",
       "      <td>978302124</td>\n",
       "      <td>Ferris Bueller's Day Off (1986)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15859</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>1035</td>\n",
       "      <td>5</td>\n",
       "      <td>978301753</td>\n",
       "      <td>Sound of Music, The (1965)</td>\n",
       "      <td>Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16741</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>2791</td>\n",
       "      <td>4</td>\n",
       "      <td>978302188</td>\n",
       "      <td>Airplane! (1980)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18472</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>2687</td>\n",
       "      <td>3</td>\n",
       "      <td>978824268</td>\n",
       "      <td>Tarzan (1999)</td>\n",
       "      <td>Animation|Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18914</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>978301777</td>\n",
       "      <td>Bambi (1942)</td>\n",
       "      <td>Animation|Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19503</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>3105</td>\n",
       "      <td>5</td>\n",
       "      <td>978301713</td>\n",
       "      <td>Awakenings (1990)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20183</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>2797</td>\n",
       "      <td>4</td>\n",
       "      <td>978302039</td>\n",
       "      <td>Big (1988)</td>\n",
       "      <td>Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21674</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>2321</td>\n",
       "      <td>3</td>\n",
       "      <td>978302205</td>\n",
       "      <td>Pleasantville (1998)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22832</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>720</td>\n",
       "      <td>3</td>\n",
       "      <td>978300760</td>\n",
       "      <td>Wallace &amp; Gromit: The Best of Aardman Animatio...</td>\n",
       "      <td>Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23270</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>1270</td>\n",
       "      <td>5</td>\n",
       "      <td>978300055</td>\n",
       "      <td>Back to the Future (1985)</td>\n",
       "      <td>Comedy|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25853</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>527</td>\n",
       "      <td>5</td>\n",
       "      <td>978824195</td>\n",
       "      <td>Schindler's List (1993)</td>\n",
       "      <td>Drama|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28157</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>2340</td>\n",
       "      <td>3</td>\n",
       "      <td>978300103</td>\n",
       "      <td>Meet Joe Black (1998)</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28501</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>48</td>\n",
       "      <td>5</td>\n",
       "      <td>978824351</td>\n",
       "      <td>Pocahontas (1995)</td>\n",
       "      <td>Animation|Children's|Musical|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28883</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>1097</td>\n",
       "      <td>4</td>\n",
       "      <td>978301953</td>\n",
       "      <td>E.T. the Extra-Terrestrial (1982)</td>\n",
       "      <td>Children's|Drama|Fantasy|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31152</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>1721</td>\n",
       "      <td>4</td>\n",
       "      <td>978300055</td>\n",
       "      <td>Titanic (1997)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32698</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>1545</td>\n",
       "      <td>4</td>\n",
       "      <td>978824139</td>\n",
       "      <td>Ponette (1996)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32771</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>745</td>\n",
       "      <td>3</td>\n",
       "      <td>978824268</td>\n",
       "      <td>Close Shave, A (1995)</td>\n",
       "      <td>Animation|Comedy|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33428</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>2294</td>\n",
       "      <td>4</td>\n",
       "      <td>978824291</td>\n",
       "      <td>Antz (1998)</td>\n",
       "      <td>Animation|Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34073</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>3186</td>\n",
       "      <td>4</td>\n",
       "      <td>978300019</td>\n",
       "      <td>Girl, Interrupted (1999)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34504</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>1566</td>\n",
       "      <td>4</td>\n",
       "      <td>978824330</td>\n",
       "      <td>Hercules (1997)</td>\n",
       "      <td>Adventure|Animation|Children's|Comedy|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34973</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>588</td>\n",
       "      <td>4</td>\n",
       "      <td>978824268</td>\n",
       "      <td>Aladdin (1992)</td>\n",
       "      <td>Animation|Children's|Comedy|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36324</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>1907</td>\n",
       "      <td>4</td>\n",
       "      <td>978824330</td>\n",
       "      <td>Mulan (1998)</td>\n",
       "      <td>Animation|Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36814</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>783</td>\n",
       "      <td>4</td>\n",
       "      <td>978824291</td>\n",
       "      <td>Hunchback of Notre Dame, The (1996)</td>\n",
       "      <td>Animation|Children's|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37204</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>1836</td>\n",
       "      <td>5</td>\n",
       "      <td>978300172</td>\n",
       "      <td>Last Days of Disco, The (1998)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37339</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>1022</td>\n",
       "      <td>5</td>\n",
       "      <td>978300055</td>\n",
       "      <td>Cinderella (1950)</td>\n",
       "      <td>Animation|Children's|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37916</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>2762</td>\n",
       "      <td>4</td>\n",
       "      <td>978302091</td>\n",
       "      <td>Sixth Sense, The (1999)</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40375</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>150</td>\n",
       "      <td>5</td>\n",
       "      <td>978301777</td>\n",
       "      <td>Apollo 13 (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41626</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>978824268</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43703</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>1961</td>\n",
       "      <td>5</td>\n",
       "      <td>978301590</td>\n",
       "      <td>Rain Man (1988)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45033</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>1962</td>\n",
       "      <td>4</td>\n",
       "      <td>978301753</td>\n",
       "      <td>Driving Miss Daisy (1989)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45685</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>2692</td>\n",
       "      <td>4</td>\n",
       "      <td>978301570</td>\n",
       "      <td>Run Lola Run (Lola rennt) (1998)</td>\n",
       "      <td>Action|Crime|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46757</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>260</td>\n",
       "      <td>4</td>\n",
       "      <td>978300760</td>\n",
       "      <td>Star Wars: Episode IV - A New Hope (1977)</td>\n",
       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49748</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>1028</td>\n",
       "      <td>5</td>\n",
       "      <td>978301777</td>\n",
       "      <td>Mary Poppins (1964)</td>\n",
       "      <td>Children's|Comedy|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50759</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>1029</td>\n",
       "      <td>5</td>\n",
       "      <td>978302205</td>\n",
       "      <td>Dumbo (1941)</td>\n",
       "      <td>Animation|Children's|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51327</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>1207</td>\n",
       "      <td>4</td>\n",
       "      <td>978300719</td>\n",
       "      <td>To Kill a Mockingbird (1962)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52255</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>2028</td>\n",
       "      <td>5</td>\n",
       "      <td>978301619</td>\n",
       "      <td>Saving Private Ryan (1998)</td>\n",
       "      <td>Action|Drama|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54908</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>531</td>\n",
       "      <td>4</td>\n",
       "      <td>978302149</td>\n",
       "      <td>Secret Garden, The (1993)</td>\n",
       "      <td>Children's|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55246</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>3114</td>\n",
       "      <td>4</td>\n",
       "      <td>978302174</td>\n",
       "      <td>Toy Story 2 (1999)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56831</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>608</td>\n",
       "      <td>4</td>\n",
       "      <td>978301398</td>\n",
       "      <td>Fargo (1996)</td>\n",
       "      <td>Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59344</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>1246</td>\n",
       "      <td>4</td>\n",
       "      <td>978302091</td>\n",
       "      <td>Dead Poets Society (1989)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       UserID Gender  Age  Occupation Zip-code  MovieID  Rating  Timestamp  \\\n",
       "0           1      F    1          10    48067     1193       5  978300760   \n",
       "1725        1      F    1          10    48067      661       3  978302109   \n",
       "2250        1      F    1          10    48067      914       3  978301968   \n",
       "2886        1      F    1          10    48067     3408       4  978300275   \n",
       "4201        1      F    1          10    48067     2355       5  978824291   \n",
       "5904        1      F    1          10    48067     1197       3  978302268   \n",
       "8222        1      F    1          10    48067     1287       5  978302039   \n",
       "8926        1      F    1          10    48067     2804       5  978300719   \n",
       "10278       1      F    1          10    48067      594       4  978302268   \n",
       "11041       1      F    1          10    48067      919       4  978301368   \n",
       "12759       1      F    1          10    48067      595       5  978824268   \n",
       "13819       1      F    1          10    48067      938       4  978301752   \n",
       "14006       1      F    1          10    48067     2398       4  978302281   \n",
       "14386       1      F    1          10    48067     2918       4  978302124   \n",
       "15859       1      F    1          10    48067     1035       5  978301753   \n",
       "16741       1      F    1          10    48067     2791       4  978302188   \n",
       "18472       1      F    1          10    48067     2687       3  978824268   \n",
       "18914       1      F    1          10    48067     2018       4  978301777   \n",
       "19503       1      F    1          10    48067     3105       5  978301713   \n",
       "20183       1      F    1          10    48067     2797       4  978302039   \n",
       "21674       1      F    1          10    48067     2321       3  978302205   \n",
       "22832       1      F    1          10    48067      720       3  978300760   \n",
       "23270       1      F    1          10    48067     1270       5  978300055   \n",
       "25853       1      F    1          10    48067      527       5  978824195   \n",
       "28157       1      F    1          10    48067     2340       3  978300103   \n",
       "28501       1      F    1          10    48067       48       5  978824351   \n",
       "28883       1      F    1          10    48067     1097       4  978301953   \n",
       "31152       1      F    1          10    48067     1721       4  978300055   \n",
       "32698       1      F    1          10    48067     1545       4  978824139   \n",
       "32771       1      F    1          10    48067      745       3  978824268   \n",
       "33428       1      F    1          10    48067     2294       4  978824291   \n",
       "34073       1      F    1          10    48067     3186       4  978300019   \n",
       "34504       1      F    1          10    48067     1566       4  978824330   \n",
       "34973       1      F    1          10    48067      588       4  978824268   \n",
       "36324       1      F    1          10    48067     1907       4  978824330   \n",
       "36814       1      F    1          10    48067      783       4  978824291   \n",
       "37204       1      F    1          10    48067     1836       5  978300172   \n",
       "37339       1      F    1          10    48067     1022       5  978300055   \n",
       "37916       1      F    1          10    48067     2762       4  978302091   \n",
       "40375       1      F    1          10    48067      150       5  978301777   \n",
       "41626       1      F    1          10    48067        1       5  978824268   \n",
       "43703       1      F    1          10    48067     1961       5  978301590   \n",
       "45033       1      F    1          10    48067     1962       4  978301753   \n",
       "45685       1      F    1          10    48067     2692       4  978301570   \n",
       "46757       1      F    1          10    48067      260       4  978300760   \n",
       "49748       1      F    1          10    48067     1028       5  978301777   \n",
       "50759       1      F    1          10    48067     1029       5  978302205   \n",
       "51327       1      F    1          10    48067     1207       4  978300719   \n",
       "52255       1      F    1          10    48067     2028       5  978301619   \n",
       "54908       1      F    1          10    48067      531       4  978302149   \n",
       "55246       1      F    1          10    48067     3114       4  978302174   \n",
       "56831       1      F    1          10    48067      608       4  978301398   \n",
       "59344       1      F    1          10    48067     1246       4  978302091   \n",
       "\n",
       "                                                   Title  \\\n",
       "0                 One Flew Over the Cuckoo's Nest (1975)   \n",
       "1725                    James and the Giant Peach (1996)   \n",
       "2250                                 My Fair Lady (1964)   \n",
       "2886                              Erin Brockovich (2000)   \n",
       "4201                                Bug's Life, A (1998)   \n",
       "5904                          Princess Bride, The (1987)   \n",
       "8222                                      Ben-Hur (1959)   \n",
       "8926                           Christmas Story, A (1983)   \n",
       "10278             Snow White and the Seven Dwarfs (1937)   \n",
       "11041                           Wizard of Oz, The (1939)   \n",
       "12759                        Beauty and the Beast (1991)   \n",
       "13819                                        Gigi (1958)   \n",
       "14006                      Miracle on 34th Street (1947)   \n",
       "14386                    Ferris Bueller's Day Off (1986)   \n",
       "15859                         Sound of Music, The (1965)   \n",
       "16741                                   Airplane! (1980)   \n",
       "18472                                      Tarzan (1999)   \n",
       "18914                                       Bambi (1942)   \n",
       "19503                                  Awakenings (1990)   \n",
       "20183                                         Big (1988)   \n",
       "21674                               Pleasantville (1998)   \n",
       "22832  Wallace & Gromit: The Best of Aardman Animatio...   \n",
       "23270                          Back to the Future (1985)   \n",
       "25853                            Schindler's List (1993)   \n",
       "28157                              Meet Joe Black (1998)   \n",
       "28501                                  Pocahontas (1995)   \n",
       "28883                  E.T. the Extra-Terrestrial (1982)   \n",
       "31152                                     Titanic (1997)   \n",
       "32698                                     Ponette (1996)   \n",
       "32771                              Close Shave, A (1995)   \n",
       "33428                                        Antz (1998)   \n",
       "34073                           Girl, Interrupted (1999)   \n",
       "34504                                    Hercules (1997)   \n",
       "34973                                     Aladdin (1992)   \n",
       "36324                                       Mulan (1998)   \n",
       "36814                Hunchback of Notre Dame, The (1996)   \n",
       "37204                     Last Days of Disco, The (1998)   \n",
       "37339                                  Cinderella (1950)   \n",
       "37916                            Sixth Sense, The (1999)   \n",
       "40375                                   Apollo 13 (1995)   \n",
       "41626                                   Toy Story (1995)   \n",
       "43703                                    Rain Man (1988)   \n",
       "45033                          Driving Miss Daisy (1989)   \n",
       "45685                   Run Lola Run (Lola rennt) (1998)   \n",
       "46757          Star Wars: Episode IV - A New Hope (1977)   \n",
       "49748                                Mary Poppins (1964)   \n",
       "50759                                       Dumbo (1941)   \n",
       "51327                       To Kill a Mockingbird (1962)   \n",
       "52255                         Saving Private Ryan (1998)   \n",
       "54908                          Secret Garden, The (1993)   \n",
       "55246                                 Toy Story 2 (1999)   \n",
       "56831                                       Fargo (1996)   \n",
       "59344                          Dead Poets Society (1989)   \n",
       "\n",
       "                                              Genres  \n",
       "0                                              Drama  \n",
       "1725                    Animation|Children's|Musical  \n",
       "2250                                 Musical|Romance  \n",
       "2886                                           Drama  \n",
       "4201                     Animation|Children's|Comedy  \n",
       "5904                 Action|Adventure|Comedy|Romance  \n",
       "8222                          Action|Adventure|Drama  \n",
       "8926                                    Comedy|Drama  \n",
       "10278                   Animation|Children's|Musical  \n",
       "11041             Adventure|Children's|Drama|Musical  \n",
       "12759                   Animation|Children's|Musical  \n",
       "13819                                        Musical  \n",
       "14006                                          Drama  \n",
       "14386                                         Comedy  \n",
       "15859                                        Musical  \n",
       "16741                                         Comedy  \n",
       "18472                           Animation|Children's  \n",
       "18914                           Animation|Children's  \n",
       "19503                                          Drama  \n",
       "20183                                 Comedy|Fantasy  \n",
       "21674                                         Comedy  \n",
       "22832                                      Animation  \n",
       "23270                                  Comedy|Sci-Fi  \n",
       "25853                                      Drama|War  \n",
       "28157                                        Romance  \n",
       "28501           Animation|Children's|Musical|Romance  \n",
       "28883                Children's|Drama|Fantasy|Sci-Fi  \n",
       "31152                                  Drama|Romance  \n",
       "32698                                          Drama  \n",
       "32771                      Animation|Comedy|Thriller  \n",
       "33428                           Animation|Children's  \n",
       "34073                                          Drama  \n",
       "34504  Adventure|Animation|Children's|Comedy|Musical  \n",
       "34973            Animation|Children's|Comedy|Musical  \n",
       "36324                           Animation|Children's  \n",
       "36814                   Animation|Children's|Musical  \n",
       "37204                                          Drama  \n",
       "37339                   Animation|Children's|Musical  \n",
       "37916                                       Thriller  \n",
       "40375                                          Drama  \n",
       "41626                    Animation|Children's|Comedy  \n",
       "43703                                          Drama  \n",
       "45033                                          Drama  \n",
       "45685                           Action|Crime|Romance  \n",
       "46757                Action|Adventure|Fantasy|Sci-Fi  \n",
       "49748                      Children's|Comedy|Musical  \n",
       "50759                   Animation|Children's|Musical  \n",
       "51327                                          Drama  \n",
       "52255                               Action|Drama|War  \n",
       "54908                               Children's|Drama  \n",
       "55246                    Animation|Children's|Comedy  \n",
       "56831                           Crime|Drama|Thriller  \n",
       "59344                                          Drama  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 合并后的每一条记录反映了每个人的年龄，职业，性别，邮编，电影ID，评分，时间戳，电影信息，电影分类等一系列信息\n",
    "# 比如我们查看用户id为1的所有信息\n",
    "data[data.UserID==1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看每一部电影不同性别的平均评分并计算分歧差值，之后排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$1,000,000 Duck (1971)</th>\n",
       "      <td>3.375000</td>\n",
       "      <td>2.761905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Night Mother (1986)</th>\n",
       "      <td>3.388889</td>\n",
       "      <td>3.352941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>2.675676</td>\n",
       "      <td>2.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <td>2.793478</td>\n",
       "      <td>2.962085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...And Justice for All (1979)</th>\n",
       "      <td>3.828571</td>\n",
       "      <td>3.689024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Gender                                F         M\n",
       "Title                                            \n",
       "$1,000,000 Duck (1971)         3.375000  2.761905\n",
       "'Night Mother (1986)           3.388889  3.352941\n",
       "'Til There Was You (1997)      2.675676  2.733333\n",
       "'burbs, The (1989)             2.793478  2.962085\n",
       "...And Justice for All (1979)  3.828571  3.689024"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看每一部电影不同性别的平均评分 data_gender接收\n",
    "data_gender=data.pivot_table(index='Title',columns='Gender',values='Rating',aggfunc='mean')\n",
    "data_gender.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$1,000,000 Duck (1971)</th>\n",
       "      <td>3.375000</td>\n",
       "      <td>2.761905</td>\n",
       "      <td>0.613095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Night Mother (1986)</th>\n",
       "      <td>3.388889</td>\n",
       "      <td>3.352941</td>\n",
       "      <td>0.035948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>2.675676</td>\n",
       "      <td>2.733333</td>\n",
       "      <td>0.057658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <td>2.793478</td>\n",
       "      <td>2.962085</td>\n",
       "      <td>0.168607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...And Justice for All (1979)</th>\n",
       "      <td>3.828571</td>\n",
       "      <td>3.689024</td>\n",
       "      <td>0.139547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Gender                                F         M      diff\n",
       "Title                                                      \n",
       "$1,000,000 Duck (1971)         3.375000  2.761905  0.613095\n",
       "'Night Mother (1986)           3.388889  3.352941  0.035948\n",
       "'Til There Was You (1997)      2.675676  2.733333  0.057658\n",
       "'burbs, The (1989)             2.793478  2.962085  0.168607\n",
       "...And Justice for All (1979)  3.828571  3.689024  0.139547"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看电影分歧最大的那部电影，在原数据中体现\n",
    "data_gender['diff']=np.fabs(data_gender.F-data_gender.M)\n",
    "data_gender.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tigrero: A Film That Was Never Made (1994)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>3.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spiders, The (Die Spinnen, 1. Teil: Der Goldene See) (1919)</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neon Bible, The (1995)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James Dean Story, The (1957)</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country Life (1994)</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enfer, L' (1994)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>2.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Babyfever (1994)</th>\n",
       "      <td>3.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stalingrad (1993)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.593750</td>\n",
       "      <td>2.593750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woman of Paris, A (1923)</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.428571</td>\n",
       "      <td>2.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cobra (1925)</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Gender                                                     F         M  \\\n",
       "Title                                                                    \n",
       "Tigrero: A Film That Was Never Made (1994)          1.000000  4.333333   \n",
       "Spiders, The (Die Spinnen, 1. Teil: Der Goldene...  4.000000  1.000000   \n",
       "Neon Bible, The (1995)                              1.000000  4.000000   \n",
       "James Dean Story, The (1957)                        4.000000  1.000000   \n",
       "Country Life (1994)                                 5.000000  2.000000   \n",
       "Enfer, L' (1994)                                    1.000000  3.750000   \n",
       "Babyfever (1994)                                    3.666667  1.000000   \n",
       "Stalingrad (1993)                                   1.000000  3.593750   \n",
       "Woman of Paris, A (1923)                            5.000000  2.428571   \n",
       "Cobra (1925)                                        4.000000  1.500000   \n",
       "\n",
       "Gender                                                  diff  \n",
       "Title                                                         \n",
       "Tigrero: A Film That Was Never Made (1994)          3.333333  \n",
       "Spiders, The (Die Spinnen, 1. Teil: Der Goldene...  3.000000  \n",
       "Neon Bible, The (1995)                              3.000000  \n",
       "James Dean Story, The (1957)                        3.000000  \n",
       "Country Life (1994)                                 3.000000  \n",
       "Enfer, L' (1994)                                    2.750000  \n",
       "Babyfever (1994)                                    2.666667  \n",
       "Stalingrad (1993)                                   2.593750  \n",
       "Woman of Paris, A (1923)                            2.571429  \n",
       "Cobra (1925)                                        2.500000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 男女电影分歧最大进行排序 data_gender_sorted接收\n",
    "data_gender_sorted=data_gender.sort_values(by='diff',ascending=False)\n",
    "data_gender_sorted.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 算出每部电影平均得分并对其进行排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$1,000,000 Duck (1971)</th>\n",
       "      <td>3.027027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Night Mother (1986)</th>\n",
       "      <td>3.371429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>2.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <td>2.910891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...And Justice for All (1979)</th>\n",
       "      <td>3.713568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Rating\n",
       "Title                                  \n",
       "$1,000,000 Duck (1971)         3.027027\n",
       "'Night Mother (1986)           3.371429\n",
       "'Til There Was You (1997)      2.692308\n",
       "'burbs, The (1989)             2.910891\n",
       "...And Justice for All (1979)  3.713568"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#算出每部电影平均得分并对其进行排序 data_mean_rating 接收\n",
    "data_mean_rating=data.pivot_table(index='Title',values='Rating',aggfunc='mean')\n",
    "data_mean_rating.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ulysses (Ulisse) (1954)</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lured (1947)</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Follow the Bitch (1998)</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bittersweet Motel (2000)</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Song of Freedom (1936)</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Rating\n",
       "Title                           \n",
       "Ulysses (Ulisse) (1954)      5.0\n",
       "Lured (1947)                 5.0\n",
       "Follow the Bitch (1998)      5.0\n",
       "Bittersweet Motel (2000)     5.0\n",
       "Song of Freedom (1936)       5.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对电影平均得分排序\n",
    "data_mean_rating_sorted=data_mean_rating.sort_values(by='Rating',ascending=False)\n",
    "data_mean_rating_sorted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看评分次数多的电影并进行排序 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title\n",
       "$1,000,000 Duck (1971)                37\n",
       "'Night Mother (1986)                  70\n",
       "'Til There Was You (1997)             52\n",
       "'burbs, The (1989)                   303\n",
       "...And Justice for All (1979)        199\n",
       "1-900 (1994)                           2\n",
       "10 Things I Hate About You (1999)    700\n",
       "101 Dalmatians (1961)                565\n",
       "101 Dalmatians (1996)                364\n",
       "12 Angry Men (1957)                  616\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看评分次数多的电影并进行排序   data_rating_num接收\n",
    "data_rating_num=data.groupby('Title').size()\n",
    "data_rating_num.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title\n",
       "American Beauty (1999)                                   3428\n",
       "Star Wars: Episode IV - A New Hope (1977)                2991\n",
       "Star Wars: Episode V - The Empire Strikes Back (1980)    2990\n",
       "Star Wars: Episode VI - Return of the Jedi (1983)        2883\n",
       "Jurassic Park (1993)                                     2672\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#进行排序\n",
    "data_rating_num_sorted=data_rating_num.sort_values(ascending=False)\n",
    "data_rating_num_sorted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# movielens电影评分数据分析(下)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 过滤掉评分条目数不足250条的电影"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title\n",
       "'burbs, The (1989)                   303\n",
       "10 Things I Hate About You (1999)    700\n",
       "101 Dalmatians (1961)                565\n",
       "101 Dalmatians (1996)                364\n",
       "12 Angry Men (1957)                  616\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#过滤掉评分条目数不足250条的电影\n",
    "hot_movies=data_rating_num[data_rating_num>250]\n",
    "hot_movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title\n",
       "American Beauty (1999)                                   3428\n",
       "Star Wars: Episode IV - A New Hope (1977)                2991\n",
       "Star Wars: Episode V - The Empire Strikes Back (1980)    2990\n",
       "Star Wars: Episode VI - Return of the Jedi (1983)        2883\n",
       "Jurassic Park (1993)                                     2672\n",
       "Saving Private Ryan (1998)                               2653\n",
       "Terminator 2: Judgment Day (1991)                        2649\n",
       "Matrix, The (1999)                                       2590\n",
       "Back to the Future (1985)                                2583\n",
       "Silence of the Lambs, The (1991)                         2578\n",
       "Men in Black (1997)                                      2538\n",
       "Raiders of the Lost Ark (1981)                           2514\n",
       "Fargo (1996)                                             2513\n",
       "Sixth Sense, The (1999)                                  2459\n",
       "Braveheart (1995)                                        2443\n",
       "Shakespeare in Love (1998)                               2369\n",
       "Princess Bride, The (1987)                               2318\n",
       "Schindler's List (1993)                                  2304\n",
       "L.A. Confidential (1997)                                 2288\n",
       "Groundhog Day (1993)                                     2278\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对评分数量进行排序，并取前20条数据\n",
    "hot_movies_sorted=hot_movies.sort_values(ascending=False)\n",
    "hot_movies_sorted[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 评分最高的十部电影\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Seven Samurai (The Magnificent Seven) (Shichinin no samurai) (1954)</th>\n",
       "      <td>628</td>\n",
       "      <td>4.560510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shawshank Redemption, The (1994)</th>\n",
       "      <td>2227</td>\n",
       "      <td>4.554558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Godfather, The (1972)</th>\n",
       "      <td>2223</td>\n",
       "      <td>4.524966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Close Shave, A (1995)</th>\n",
       "      <td>657</td>\n",
       "      <td>4.520548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Usual Suspects, The (1995)</th>\n",
       "      <td>1783</td>\n",
       "      <td>4.517106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schindler's List (1993)</th>\n",
       "      <td>2304</td>\n",
       "      <td>4.510417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wrong Trousers, The (1993)</th>\n",
       "      <td>882</td>\n",
       "      <td>4.507937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)</th>\n",
       "      <td>470</td>\n",
       "      <td>4.491489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raiders of the Lost Ark (1981)</th>\n",
       "      <td>2514</td>\n",
       "      <td>4.477725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rear Window (1954)</th>\n",
       "      <td>1050</td>\n",
       "      <td>4.476190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Rating          \n",
       "                                                     size      mean\n",
       "Title                                                              \n",
       "Seven Samurai (The Magnificent Seven) (Shichini...    628  4.560510\n",
       "Shawshank Redemption, The (1994)                     2227  4.554558\n",
       "Godfather, The (1972)                                2223  4.524966\n",
       "Close Shave, A (1995)                                 657  4.520548\n",
       "Usual Suspects, The (1995)                           1783  4.517106\n",
       "Schindler's List (1993)                              2304  4.510417\n",
       "Wrong Trousers, The (1993)                            882  4.507937\n",
       "Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)         470  4.491489\n",
       "Raiders of the Lost Ark (1981)                       2514  4.477725\n",
       "Rear Window (1954)                                   1050  4.476190"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#评分最高的十部电影\n",
    "movies_stats=data.groupby('Title').agg({'Rating':[np.size,np.mean]})\n",
    "movies_stats.head(10)\n",
    "# 被评论的次数>=100\n",
    "atleast_100=movies_stats['Rating']['size']>=100\n",
    "movies_stats[atleast_100].sort_values([('Rating','mean')],ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看不同年龄的分布情况并且采用直方图进行可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28c13660e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "users.Age.plot.hist(bins=30)\n",
    "plt.title('users_ages')\n",
    "plt.xlabel('age')\n",
    "plt.ylabel('count of age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 在原数据中标记出用户位于的年龄分组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28c14005550>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels=['0-9','10-19','20-29','30-39','40-49','50-59','60-69','70-79']\n",
    "data['age_group']=pd.cut(data.Age,range(0,81,10),labels=labels)\n",
    "data.head()\n",
    "data['age_group'].value_counts().plot(kind='bar')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 电影评分表中计算不同类型电影的频数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MovieID</th>\n",
       "      <th>level_1</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Children's</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MovieID  level_1           0\n",
       "0        1        0   Animation\n",
       "1        1        1  Children's\n",
       "2        1        2      Comedy\n",
       "3        2        0   Adventure\n",
       "4        2        1  Children's"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对数据进行规整-movies\n",
    "movie_clean_1=pd.DataFrame(movies.Genres.str.split('|').tolist(),index=movies.MovieID)\n",
    "movie_clean_1.head()\n",
    "movie_clean_2=movie_clean_1.stack().reset_index()\n",
    "movie_clean_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MovieID</th>\n",
       "      <th>Genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Children's</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MovieID      Genres\n",
       "0        1   Animation\n",
       "1        1  Children's\n",
       "2        1      Comedy\n",
       "3        2   Adventure\n",
       "4        2  Children's"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#删除level_1列，将columns为0的列重命名为genres,并重新定义数据框为movies_genres\n",
    "movies_genres=movie_clean_2.drop('level_1',axis=1).rename(columns={0:'Genres'})\n",
    "movies_genres.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MovieID</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MovieID                     Title      Genres\n",
       "0        1          Toy Story (1995)   Animation\n",
       "1        1          Toy Story (1995)  Children's\n",
       "2        1          Toy Story (1995)      Comedy\n",
       "3        2            Jumanji (1995)   Adventure\n",
       "4        2            Jumanji (1995)  Children's\n",
       "5        2            Jumanji (1995)     Fantasy\n",
       "6        3   Grumpier Old Men (1995)      Comedy\n",
       "7        3   Grumpier Old Men (1995)     Romance\n",
       "8        4  Waiting to Exhale (1995)      Comedy\n",
       "9        4  Waiting to Exhale (1995)       Drama"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将原movies数据中的genres列替换成movies_genres，得到规整化处理后的movies数据 \n",
    "movies=pd.merge(movies.drop('Genres',axis=1),movies_genres)\n",
    "movies.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserID</th>\n",
       "      <th>MovieID</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978300760</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978298413</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>1193</td>\n",
       "      <td>4</td>\n",
       "      <td>978220179</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>1193</td>\n",
       "      <td>4</td>\n",
       "      <td>978199279</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978158471</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UserID  MovieID  Rating  Timestamp Genres\n",
       "0       1     1193       5  978300760  Drama\n",
       "1       2     1193       5  978298413  Drama\n",
       "2      12     1193       4  978220179  Drama\n",
       "3      15     1193       4  978199279  Drama\n",
       "4      17     1193       5  978158471  Drama"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#合并。构建电影评分数据集movie_ratings\n",
    "movie_ratings=ratings.merge(movies.drop('Title',axis=1),how='inner',on='MovieID')\n",
    "movie_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28c14066a58>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#计算movies_ratings中不同类型电影的频数\n",
    "movies_ratings_sorted=movie_ratings.groupby(['Genres'])['MovieID'].size()\n",
    "movies_ratings_sorted.sort_values(ascending=False).plot(kind='bar')\n",
    "# movies_ratings_sorted.\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
